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dc.contributor.authorSahli, Majed
dc.contributor.authorMansour, Essam
dc.contributor.authorKalnis, Panos
dc.date.accessioned2015-08-03T12:09:58Z
dc.date.available2015-08-03T12:09:58Z
dc.date.issued2014-10-02
dc.identifier.issn10668888
dc.identifier.doi10.1007/s00778-014-0370-1
dc.identifier.urihttp://hdl.handle.net/10754/563784
dc.description.abstractModern applications, including bioinformatics, time series, and web log analysis, require the extraction of frequent patterns, called motifs, from one very long (i.e., several gigabytes) sequence. Existing approaches are either heuristics that are error-prone, or exact (also called combinatorial) methods that are extremely slow, therefore, applicable only to very small sequences (i.e., in the order of megabytes). This paper presents ACME, a combinatorial approach that scales to gigabyte-long sequences and is the first to support supermaximal motifs. ACME is a versatile parallel system that can be deployed on desktop multi-core systems, or on thousands of CPUs in the cloud. However, merely using more compute nodes does not guarantee efficiency, because of the related overheads. To this end, ACME introduces an automatic tuning mechanism that suggests the appropriate number of CPUs to utilize, in order to meet the user constraints in terms of run time, while minimizing the financial cost of cloud resources. Our experiments show that, compared to the state of the art, ACME supports three orders of magnitude longer sequences (e.g., DNA for the entire human genome); handles large alphabets (e.g., English alphabet for Wikipedia); scales out to 16,384 CPUs on a supercomputer; and supports elastic deployment in the cloud.
dc.publisherSpringer Nature
dc.subjectAutomatic tuning
dc.subjectCache efficient
dc.subjectCloud
dc.subjectElastic
dc.subjectMotif
dc.subjectSuffix tree
dc.titleACME: A scalable parallel system for extracting frequent patterns from a very long sequence
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalThe VLDB Journal
dc.contributor.institutionQatar Computing Research InstituteDoha, Qatar
kaust.personKalnis, Panos
kaust.personSahli, Majed
dc.date.published-online2014-10-02
dc.date.published-print2014-12


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